Auto-triage
Auto-triage automatically handles the first layer of triage: labelling, prioritizing, summarizing, and surfacing similar issues.
Auto-labelling
When a new thread is created, Plain AI will automatically assign 1–2 labels based on the content of the request. This helps route and filter requests instantly, without waiting for manual input.
If a thread is already labelled (via API or form), auto-labelling won’t override it. To make the most of auto-labelling, set up your labels first in Settings → Labels.
If you want to exclude a label from auto-labelling, you can uncheck the Can be applied by Plain AI setting on the label.
We recommend creating labels for generic requests as well as a few that are specific to your product. Examples of generic requests include:
Feature Request
Bug
Billing
Demo Request
Other
Thread titles
In Slack and other real-time channels, support requests often lack structure. Auto-generated thread titles solve this by creating short, descriptive summaries for each conversation.
This makes your inbox easier to scan, helps teammates jump in with context, and improves searchability, especially when requests come in without a clear subject line.
Urgency detection (Beta)
Plain AI automatically detects when a support request is urgent, flagging threads that mention downtime, blockers, or critical impact.
Urgent threads are marked clearly in the UI, which is particularly valuable when:
Customers don’t explicitly say a request is urgent, but their message suggests it.
You’re dealing with outages or high-priority bugs and need to triage at speed.
You’re coordinating between support and engineering in real time.
With urgency detection, your team stays focused on what matters most – no manual tagging or Slack escalations required.
Thread catch-ups
Long-running threads can be hard to catch up on.
Plain AI automatically generates and keeps an up-to-date summary for each thread, so you can quickly understand what’s happened without re-reading the full timeline. You’ll find the thread catch-up at the bottom of the thread timeline.
Each catch-up includes context from customer-facing messages, internal notes, thread discussions, and linked issues. It’s designed to help you pick up work confidently during handovers, busy shifts, or when returning to a thread after time away.
We also log feedback on each catch-up and its suggested next steps to continuously improve accuracy.
You can turn thread catch-ups on or off for your workspace in AI → Configuration → Context and metadata → Thread catch-ups. If catch-ups aren’t appearing, check that the feature is enabled for your workspace and that the thread has recent activity.
Similar threads (Beta)
To prevent duplicate work and speed up resolution, Plain AI can show similar threads in the thread details panel.
This helps your team:
See if an issue has already been solved
Reuse proven replies or workflows
Spot patterns across customers or issues
By connecting the dots across your support history, this feature helps every teammate act with the confidence of someone who’s seen it all before.